import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras import backend as K

# 类别数量确定了输出层的神经元数量
num_classes = 10

# 批次大小
batch_size = 128

# 训练轮数
epochs = 12

# 确定输入图像的维度
img_rows, img_cols = 28, 28

# 使用load_data, 在导入数据时可以把测试集与训练集直接分开(好耶)
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# 使用backend后台的K.image_data_format()获取通道在通道中的位置是一个不错的选择
if K.image_data_format() == 'channels_first':
    x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)
    x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)
    input_shape = (1, img_rows, img_cols)
else:
    x_train = x_train.reshape(x_train.shape[0],  img_rows, img_cols, 1)
    x_test = x_test.reshape(x_test.shape[0],img_rows, img_cols, 1)
    input_shape = ( img_rows, img_cols, 1)

# 转换数据类型
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255

# 打印形状
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# 把一维的类别向量转化成二值向量形式
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

# 初始化模型
model = Sequential()

# 逐层添加神经层
model.add(Conv2D(32, kernel_size=(3, 3), activation= 'relu', input_shape = input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

# 模型编译，定义了交叉熵损失，优化器和精度指标
model.compile(loss = keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adadelta(), metrics=['accuracy'])

# 开始训练
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, validation_data=(x_test, y_test))

#模型评估
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])

# 使之图像化
import matplotlib.pyplot as plt

# 列出history中所有关键字
print(history.history.keys())

# 显示准确度
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc = 'upper left')
plt.show()

# 显示loss
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc= 'upper left')
plt.show()
